AI governance is how an organisation keeps its use of artificial intelligence safe, lawful and accountable. It is the framework of policies, roles and controls that decides where AI is used, who answers for it, and how its risks are managed. This guide from the Institute of AI sets out what good governance looks like and how to put it in place.
A plain answer first, because most boards are researching this while the technology is already in the building.
AI governance is the system of rules, roles and controls an organisation uses to make sure its artificial intelligence is safe, legal, fair and accountable. It answers three questions that boards cannot afford to leave open: where is AI being used, who is responsible for each use, and how do we know it is behaving as intended. Governance is not a document you write once. It is an operating discipline that runs for as long as the systems do.
The framework itself is usually built from a policy that states principles and acceptable use, a register of every AI system in play, a consistent way to assess and grade risk, and the technical and human controls that keep higher-risk uses in check. In the UK this sits alongside established obligations, particularly data protection under UK GDPR, rather than replacing them. Good AI governance makes existing duties easier to meet, not harder.
Crucially, governance assigns responsibility to people, not to the technology. A model cannot be held to account, so accountability has to rest with named individuals, from the board that sets the risk appetite to the employee who checks an output before acting on it. The Institute of AI, the UK's professional body for AI, exists to define that standard and help organisations meet it. The sections below break the framework into its parts and set out who owns what.
A working framework is made of concrete, named parts. Miss one and the gap tends to be exactly where the next problem appears.
A written statement of how the organisation will and will not use AI, tied to its values and its legal duties. Principles turn good intentions into a standard people can be held to, covering acceptable use, prohibited use, and the outcomes the organisation is unwilling to risk.
A living register of every system that uses AI, whether built in-house, bought in, or embedded in a wider product. You cannot govern what you cannot see, and shadow AI adopted quietly by teams is where most uncontrolled risk actually sits.
A consistent way to rate each use by its potential for harm, from low-stakes drafting to decisions that affect a person's money, health or rights. Higher-risk uses earn tighter controls, human oversight and, where the law requires it, a formal impact assessment.
Clear rules on what data may train, prompt or fine-tune a model, on what lawful basis, and with what retention. This is where AI governance meets UK GDPR: minimise personal data, document the purpose, and never treat a customer record as prompt fuel.
Structured checks on the models and suppliers you rely on: training provenance, licensing, security, bias testing and contractual commitments. Buying AI does not outsource the accountability, so the diligence has to be yours.
Ongoing measurement of how deployed systems actually behave, with logging, performance review and a clear route to escalate or switch a system off. Governance that stops at launch is theatre; the controls have to run for the life of the system.
Governance fails when responsibility is assumed rather than assigned. These are the roles that make it work, from the boardroom to the keyboard.
Sets the risk appetite and owns AI governance as oversight, not delegation. It signs off the governance policy, holds management to account for every system on the register, and answers to regulators and shareholders when something fails. It does not need to be technical; it needs to know which questions to ask and to insist on evidence rather than reassurance.
The senior owner of the framework itself: the policy, the inventory, the risk process and the controls that hang off them. They resource governance, keep it moving at the pace of adoption, and make sure it runs as a working operating discipline rather than a document that was signed once and filed.
Runs the machinery: the risk methodology, the pre-deployment reviews, the audit trail, and the check against changing regulation. They decide which uses count as high-risk and what controls those uses must carry. Deliberately independent of the teams whose systems they assess.
Owns the overlap between the framework and data protection law: lawful basis, impact assessments, and the individual rights an AI system can touch. In the UK this is the role that stops AI governance and established data protection duties from being run as two separate books.
Operate the systems to the standard the framework sets. They own the technical controls the register promises: access, logging, testing for bias and drift, and the ability to explain a decision or switch a model off. A control the framework claims but engineering cannot evidence is not a control.
Works inside the policy, records AI use where the framework asks for it, and escalates when something looks off. Governance only holds if the person at the keyboard knows the rules and why they exist. Live controls that everyone follows beat an immaculate framework nobody feels bound by.
You do not have to build a governance framework from scratch. The Institute of AI offers three practical routes, from a free public commitment to hands-on advisory.
A public commitment to getting your organisation ready for AI, set out as five practical pledges covering AI literacy, responsible use, skills investment, open knowledge and inclusive access. Signing is free and you choose at least three; its Responsible AI Use pledge asks for a published policy and a named owner, which is where governance starts.
Sign the CharterIndependent assessment of your organisation's AI practice against the Institute of AI's standard. Accreditation moves you from a stated intention to an externally verified one, giving clients, regulators and partners assurance that your governance is real.
Explore accreditationPractical, vendor-neutral guidance to design a governance framework that fits your size, sector and risk, and to build the internal capability to run it. Advice from an independent professional body that resells no third-party software and takes no implementation kickbacks.
Talk to an adviserStart with a free, public commitment to getting your organisation ready for AI. Signing the UK AI Readiness Charter is a credible first step towards governance you can prove.
Five practical pledges on getting your organisation ready for AI, responsible use among them. Free to sign and a credible first step in your governance.
Read the CharterIndependent assessment of your governance against the Institute of AI standard, evidence a board and a regulator can rely on.
Explore accreditationVendor-neutral guidance to design a governance framework that fits your size, sector and risk, and to run it well.
Talk to an adviser
